Priority-Based Protection Against the Malicious Data Injection Attacks on State Estimation

In the modern age, the Internet of Things forms the basis of all the infrastructures for improving efficiency, reliability, and comfort. As applied to the power system, increasing the penetration of information and communication technologies at the device and process level is enabling devices to communicate with each other, thereby, facilitating wide-area situational awareness of the power grid. Dependence on the communication infrastructure has also made the power grid vulnerable to cyber-intrusions. Cybersecurity of the power system is a huge concern, which must be addressed with topmost priority. Securing all the entry points for cyber-attacks may not be economically feasible; hence, a critical set of sensors must be identified and secured to protect the grid against malicious actors. In this article, we propose a defense strategy to select the most critical measurements for securing the power system operation against false data injection attacks. The proposed approach is compared with the existing methodologies in the literature and validated using standard IEEE test cases.

[1]  Rongxing Lu,et al.  Defending Against False Data Injection Attacks on Power System State Estimation , 2017, IEEE Transactions on Industrial Informatics.

[2]  Wei Yu,et al.  On False Data-Injection Attacks against Power System State Estimation: Modeling and Countermeasures , 2014, IEEE Transactions on Parallel and Distributed Systems.

[3]  Mehul Motani,et al.  Detecting False Data Injection Attacks in AC State Estimation , 2015, IEEE Transactions on Smart Grid.

[4]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[5]  Zuyi Li,et al.  Quantitative Analysis of Load Redistribution Attacks in Power Systems , 2012, IEEE Transactions on Parallel and Distributed Systems.

[6]  A.J. Monticelli,et al.  Fast decoupled state estimators , 1989, Conference Papers Power Industry Computer Application Conference.

[7]  Klara Nahrstedt,et al.  Detecting False Data Injection Attacks on DC State Estimation , 2010 .

[8]  Zuyi Li,et al.  Trilevel Modeling of Cyber Attacks on Transmission Lines , 2017, IEEE Transactions on Smart Grid.

[9]  Gabriela Hug,et al.  Vulnerability Assessment of AC State Estimation With Respect to False Data Injection Cyber-Attacks , 2012, IEEE Transactions on Smart Grid.

[10]  Danda B. Rawat,et al.  Detection of False Data Injection Attacks in Smart Grid Communication Systems , 2015, IEEE Signal Processing Letters.

[11]  A. Monticelli,et al.  Fast Decoupled State Estimation and Bad Data Processing , 1979, IEEE Transactions on Power Apparatus and Systems.

[12]  Peng Ning,et al.  False data injection attacks against state estimation in electric power grids , 2009, CCS.

[13]  Zhu Han,et al.  Detecting False Data Injection Attacks on Power Grid by Sparse Optimization , 2014, IEEE Transactions on Smart Grid.

[14]  Zhihua Qu,et al.  Enhanced protection against false data injection by dynamically changing information structure of microgrids , 2012, 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM).

[15]  Bruno Sinopoli,et al.  False Data Injection Attacks in Electricity Markets , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[16]  Anupam Joshi,et al.  Bi-level Modelling of False Data Injection Attacks on Security Constrained Optimal Power Flow , 2017 .

[17]  Chen-Ching Liu,et al.  Intruders in the Grid , 2012, IEEE Power and Energy Magazine.

[18]  Ying Jun Zhang,et al.  Defending mechanisms against false-data injection attacks in the power system state estimation , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[19]  Zuyi Li,et al.  Local Load Redistribution Attacks in Power Systems With Incomplete Network Information , 2014, IEEE Transactions on Smart Grid.

[20]  Anupam Joshi,et al.  Data integrity attack in smart grid: optimised attack to gain momentary economic profit , 2016 .

[21]  Pierluigi Siano,et al.  Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow , 2012, IEEE Transactions on Industrial Informatics.

[22]  Linqiang Ge,et al.  A novel architecture against false data injection attacks in smart grid , 2012, 2012 IEEE International Conference on Communications (ICC).

[23]  Bruno Sinopoli,et al.  Integrity Data Attacks in Power Market Operations , 2011, IEEE Transactions on Smart Grid.

[24]  B. K. Panigrahi,et al.  Joint-Transformation-Based Detection of False Data Injection Attacks in Smart Grid , 2018, IEEE Transactions on Industrial Informatics.

[25]  Ali Tajer,et al.  False Data Injection Attacks in Electricity Markets by Limited Adversaries: Stochastic Robustness , 2019, IEEE Transactions on Smart Grid.

[26]  Siddharth Sridhar,et al.  Cyber–Physical System Security for the Electric Power Grid , 2012, Proceedings of the IEEE.

[27]  Anupam Joshi,et al.  AI based approach to identify compromised meters in data integrity attacks on smart grid , 2017 .

[28]  Lang Tong,et al.  Limiting false data attacks on power system state estimation , 2010, 2010 44th Annual Conference on Information Sciences and Systems (CISS).

[29]  Lang Tong,et al.  On malicious data attacks on power system state estimation , 2010, 45th International Universities Power Engineering Conference UPEC2010.

[30]  Aditya Ashok,et al.  Online Detection of Stealthy False Data Injection Attacks in Power System State Estimation , 2018, IEEE Transactions on Smart Grid.

[31]  A. G. Expósito,et al.  Power system state estimation : theory and implementation , 2004 .

[32]  Abdullah Abusorrah,et al.  Bilevel Model for Analyzing Coordinated Cyber-Physical Attacks on Power Systems , 2016, IEEE Transactions on Smart Grid.